Tensor completion throughmultiple Kronecker product decomposition

Anh Huy Phan, Andrzej Cichocki, Petr Tichavsky, Gheorghe Luta, Austin Brockmeier

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

14 Citations (Scopus)

Abstract

We propose a novel decomposition approach to impute missing values in tensor data. The method uses smaller scale multiway patches to model the whole data or a small volume encompassing the observed missing entries. Simulations on color images show that our method can recover color images using only 5-10% of pixels, and outperforms other available tensor completion methods.

Original languageEnglish
Title of host publication2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings
Pages3233-3237
Number of pages5
DOIs
Publication statusPublished - 18 Oct 2013
Externally publishedYes
Event2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Vancouver, BC, Canada
Duration: 26 May 201331 May 2013

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Conference

Conference2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013
Country/TerritoryCanada
CityVancouver, BC
Period26/05/1331/05/13

Keywords

  • color image
  • Kronecker tensor decomposition (KTD)
  • tensor completion
  • tensor decomposition

Fingerprint

Dive into the research topics of 'Tensor completion throughmultiple Kronecker product decomposition'. Together they form a unique fingerprint.

Cite this